社交媒体数据的改进决策树分类(IDT)算法

Anu Sharma, M. K. Sharma, R. K. Dwivedi
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引用次数: 1

摘要

本文将分类算法应用于社交网络。我们提出了一种新的分类算法,称为改进决策树(IDT)。我们的模型提供了比现有的分类系统更好的分类精度,用于分类社会网络数据。在这里,我们检查了一些熟悉的分类算法的性能,以及它们与我们提出的算法的准确性。我们在研究中使用Naïve贝叶斯、SVM、KNN、决策树,并对社交媒体数据集进行分析。使用Matlab进行实验。结果表明,该算法达到了最佳效果,准确率为84.66%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improved Decision Tree Classification (IDT) Algorithm for Social Media Data
In this paper we used classification algorithms on social networking. We are proposing, a new classification algorithm called the improved Decision Tree (IDT). Our model provides better classification accuracy than the existing systems for classifying the social network data. Here we examined the performance of some familiar classification algorithms regarding their accuracy with our proposed algorithm. We used Naïve Bayes ,SVM, , KNN, decision tree in our research and performed analyses on social media dataset. Matlab is used for performing experiments. The result shows that the proposed algorithm achieves the best results with an accuracy of 84.66%.
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